CN113837523A - A method of community service quality evaluation based on natural language processing algorithm - Google Patents
A method of community service quality evaluation based on natural language processing algorithm Download PDFInfo
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Abstract
本发明公开了一种基于自然语言处理算法的社区服务质量评价的方法，涉及社区服务评价技术领域。本发明社区服务质量评价的方法，包括以下步骤：Step1：利用摄像头进行社区信息采集，建立数据模型，并生成待处理图像，同时对该图像进行信息加密；Step2：基于计算机处理设备，于计算机输入对应社区信息比对的待处理图像；Step3：对视频图像进行身份验证，并通过验证；Step4：对视频图像进行校准，检测待处理图像中的处理区域，并对社区服务的关键特征进行标记；Step5：建立服务评价模型，提取服务行为的关键特征数据，确立特征数据比对的阈值。本发明采用图像处理技术，通过利用自然语言算法进行社区服务信息计算比对，提升社区服务信息处理的精度及完成度。
The invention discloses a community service quality evaluation method based on a natural language processing algorithm, and relates to the technical field of community service evaluation. The method for evaluating the quality of community service according to the present invention includes the following steps: Step 1: use a camera to collect community information, establish a data model, generate an image to be processed, and encrypt the information at the same time; The image to be processed corresponding to the community information comparison; Step 3: Verify the identity of the video image, and pass the verification; Step 4: Calibrate the video image, detect the processing area in the image to be processed, and mark the key features of community services; Step 5: Establish a service evaluation model, extract key feature data of service behavior, and establish a threshold for feature data comparison. The invention adopts the image processing technology, and improves the accuracy and completion of the community service information processing by using the natural language algorithm to calculate and compare the community service information.
Description
Technical Field
The invention relates to the technical field of community service evaluation, in particular to a community service quality evaluation method based on a natural language processing algorithm.
Background
The community service refers to public service and other services in aspects of material, culture, life and the like which are directly provided for community members by other aspects of power such as governments, community living committees, digital communities and the like;
natural Language Processing (NLP) is an important direction in the fields of computer science and artificial intelligence, and is used for researching various theories and methods for realizing effective communication between human and computer by using Natural Language, and is a science integrating linguistics, computer science and mathematics, so that the research in this field relates to Natural Language, namely the Language used by people in daily life, and is closely related to the research of linguistics, but has important difference, and the Natural Language Processing is not generally used for researching Natural Language, but is used for developing computer systems capable of effectively realizing Natural Language communication, in particular to software systems therein, so that the computer systems are part of computer science, and the Natural Language Processing is mainly applied to machine translation, public opinion monitoring, automatic summarization, Viewpoint extraction, text classification, question answering, text semantic comparison, voice recognition, Chinese OCR and the like;
at present, the evaluation of community service quality has certain limitations, the calculation completion degree of an evaluation system is not high, the quality evaluation effect is not good, and the evaluation efficiency needs to be further improved; therefore, a method for evaluating community service quality based on a natural language processing algorithm is provided.
Disclosure of Invention
The present invention is directed to a method for evaluating community service quality based on natural language processing algorithm, so as to solve the problems set forth in the background above.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a community service quality evaluation method based on a natural language processing algorithm, which comprises the following steps:
step 1: the method comprises the steps of utilizing a camera to collect community information, establishing a data model, generating an image to be processed, and simultaneously encrypting the image;
step 2: based on computer processing equipment, inputting an image to be processed, which is compared with corresponding community information, into a computer;
step 3: performing identity authentication on the video image, and passing the authentication;
step 4: calibrating the video image, detecting a processing area in the image to be processed, and marking key features of community service;
step 5: establishing a service evaluation model, extracting key characteristic data of service behaviors, and determining a threshold value for comparing the characteristic data;
step 6: based on the calculation of a natural language processing algorithm, performing similarity comparison of behavior specifications on the images, and forming screening in a community service behavior range through a graph processing model;
step 7: screening out community behavior information which meets the specification, carrying out identity verification again, and importing the behavior information into a corresponding database;
step 8: and evaluating the community service quality.
Preferably, in Step1, in the community information collection process, a high definition camera is used to collect community service behavior information.
Preferably, in Step2, the image to be processed is specifically a scene image of at least one frame or two frames.
Preferably, in Step3, the authentication of the image is based on a computer, the authentication mode includes digital password authentication and biometric fingerprint authentication, the authentication includes an access module and an access forbidding module of a computer authentication system, the access module is connected with the processing unit of the image, and the access forbidding module is connected with the jumping unit of the main interface.
Preferably, in Step5, extracting key feature data of the community behaviors, establishing a threshold for feature data comparison, and substituting the threshold in the image processing model for the behavior data and the comparison data by completing establishment of a verification sample.
Preferably, in Step6, based on a natural language processing algorithm, the service behavior in the image to be processed is compared by using a service quality evaluation model database, and the image is screened according to the score of the service quality evaluation and the screened result image is output.
Preferably, in Step7, the screened community service comparison information conforming to the characteristics is subjected to identity verification through a digital password, and is logged in an identity information database, and service information is subjected to data import.
Preferably, in Step8, after the user finishes using and exits the program, the login verification information of the user is automatically erased, and the user needs to be verified again when logging again.
The invention has the following beneficial effects:
the community service quality evaluation method based on the natural language processing algorithm adopts the image processing technology, and improves the precision and the completion degree of community service information processing by utilizing the natural language algorithm to calculate and compare community service information.
The method for evaluating the community service quality based on the natural language processing algorithm, disclosed by the invention, is used for sorting the information compared with the community service quality based on the database and realizing continuous expansion of the information compared with the database, so that data samples compared with the community service are increased, and the comparison efficiency is favorably improved.
The community service quality evaluation method based on the natural language processing algorithm has the advantages of simple and convenient operation process, low operation cost, high operation efficiency and certain popularization value.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for evaluating community service quality based on natural language processing algorithm according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Please refer to fig. 1: the invention relates to a community service quality evaluation method based on a natural language processing algorithm, which comprises the following steps:
step 1: the method comprises the steps of utilizing a camera to collect community information, establishing a data model, generating an image to be processed, and simultaneously encrypting the image;
step 2: based on computer processing equipment, inputting an image to be processed, which is compared with corresponding community information, into a computer;
step 3: performing identity authentication on the video image, and passing the authentication;
step 4: calibrating the video image, detecting a processing area in the image to be processed, and marking key features of community service;
step 5: establishing a service evaluation model, extracting key characteristic data of service behaviors, and determining a threshold value for comparing the characteristic data;
step 6: based on the calculation of a natural language processing algorithm, performing similarity comparison of behavior specifications on the images, and forming screening in a community service behavior range through a graph processing model;
step 7: screening out community behavior information which meets the specification, carrying out identity verification again, and importing the behavior information into a corresponding database;
step 8: and evaluating the community service quality.
In Step1, in the community information acquisition process, the high-definition camera is used for acquiring community service behavior information.
In Step2, the image to be processed is specifically a scene image of at least one frame or two frames.
In Step3, the image authentication is based on a computer, the authentication mode includes digital password authentication and biometric fingerprint authentication, the authentication includes an access module and a forbidding module of a computer authentication system, the access module is connected with the image processing unit, and the forbidding module is connected with the jumping unit of the main interface.
In Step5, extracting key feature data of community behaviors, establishing a threshold value for feature data comparison, and substituting the threshold value in the image processing model for the behavior data and the comparison data by completing establishment of a verification sample.
In Step6, based on a natural language processing algorithm, a service quality evaluation model database is adopted to compare service behaviors in the image to be processed, screening is carried out according to the score of service quality evaluation, and a screened result image is output.
And in Step7, performing identity verification through a digital password on the screened community service comparison information conforming to the characteristics, logging in an identity information database, and importing the service information into the database.
In Step8, after the user finishes using and exits the program, the login verification information of the user is automatically erased, and the user needs to be verified again when logging again.
In the scheme, the image processing quality detection comprises the steps of judging whether the collected human face meets the standard quality requirement or not, and finding out a high-quality human face photo by adopting a multi-dimensional quality judgment model; the quality monitoring model comprehensively judges through a plurality of dimensions such as shielding (shielding proportion of each part of the face), fuzziness (definition of the face), illumination (illumination intensity of the face), integrity (integrity of the face), posture (angle distribution of the face in a three-dimensional space), expression (normality of the face expression) and the like;
in this scenario, the natural language processing algorithm includes 1.HMM (hidden Markov model)
HMM where x ═ is a hidden state sequence (q1, q 2.., qN) and y ═ is an observation sequence (o1, o 2.., oN), the problem requiring prediction is: (q1, q 2.,. qN) ═ argmaxP (q1, q 2.,. qN | o1, o 2.,. on), where argmax is a function, and the maximum value corresponding to P (x1, x2, x 3.,. Xn) is taken when the argument y1, y 2.,. yn takes a fixed value.
HMM is a five-tuple (O, Q, O0, A, B):
o { O1, O2, …, ot } is a set of states, also called observation sequences.
Q { Q1, Q2, …, qv } is a set of output results, also called hidden sequences.
Aij ═ P (qj | qi): transition probability distribution
Bij ═ P (oj | qi): distribution of emission probability
O0 is an initial state, and some are terminated
In the observation sequence, it can be deduced to be a hidden sequence
2. Viterbi algorithm (Vitebe)
The Viterbi algorithm is actually a dynamic path optimization algorithm, the digital communication, the voice recognition, the machine translation, the pinyin conversion to Chinese characters, the word segmentation and the like of the current technology all have the shadow of the Viterbi algorithm, the main idea of the Viterbi algorithm is to obtain the optimal path from the shortest path, dynamically plan the algorithm path and reversely push out the path, and the algorithm can greatly reduce path planning and obtain the optimal path through the shortest path;
EM algorithm
EM algorithm is a large class of algorithms in the field of machine learning
4. Logistic regression algorithm (LR algorithm)
The logistic regression algorithm is mainly used for solving the classification problem;
in the scheme, the image processing comprises image transformation, image coding compression, image enhancement and restoration, image segmentation, image description and image classification (identification), wherein the image transformation method, such as indirect processing techniques like fourier transform, walsh transform, discrete cosine transform and the like, converts the processing of the spatial domain into the processing of the transform domain, which not only can reduce the amount of computation, but also can obtain more effective processing (such as digital filtering processing in the frequency domain by fourier transform); image coding compression techniques can reduce the amount of data (i.e., the number of bits) describing an image in order to save image transmission, processing time, and reduce the amount of memory occupied; the purpose of image enhancement and restoration is to improve the quality of an image, such as removing noise, improving the definition of the image and the like, the image enhancement does not consider the reason of image degradation, and highlights the interested part in the image, such as strengthening the high-frequency component of the image, so that the outline of an object in the image is clear, the details are obvious, and the noise influence in the image can be reduced if strengthening the low-frequency component; the image segmentation is to extract the meaningful characteristic parts in the image, wherein the meaningful characteristics comprise edges, areas and the like in the image, which are the basis for further image recognition, analysis and understanding; the image description is a necessary premise for image recognition and understanding, the simplest binary image can describe the characteristics of an object by using the geometric characteristics of the binary image, the description method of a general image adopts two-dimensional shape description, and has two types of methods of boundary description and region description, the two-dimensional texture characteristic description can be used for a special texture image, and with the deep development of image processing research, the research of three-dimensional object description is started, and methods such as volume description, surface description, generalized cylinder description and the like are proposed; image classification (recognition) belongs to the category of pattern recognition, and the main content of the image classification (recognition) is that after certain preprocessing (enhancement, restoration and compression), image segmentation and characteristic extraction are carried out, so that judgment and classification are carried out, and the image classification usually adopts a classical pattern recognition method and has statistical pattern classification and syntactic (structural) pattern classification.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
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| CN114529905A (en) * | 2022-01-27 | 2022-05-24 | 北京开拓鸿业高科技有限公司 | Pinyin identification method and device, storage medium and electronic equipment |
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| CN110363154A (en) * | 2019-07-17 | 2019-10-22 | 安徽航天信息有限公司 | A kind of service quality examining method and system based on Emotion identification |
| CN112183417A (en) * | 2020-09-30 | 2021-01-05 | 重庆天智慧启科技有限公司 | Business consultant service capability evaluation system and method |
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| CN110363154A (en) * | 2019-07-17 | 2019-10-22 | 安徽航天信息有限公司 | A kind of service quality examining method and system based on Emotion identification |
| CN112183417A (en) * | 2020-09-30 | 2021-01-05 | 重庆天智慧启科技有限公司 | Business consultant service capability evaluation system and method |
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| CN114529905A (en) * | 2022-01-27 | 2022-05-24 | 北京开拓鸿业高科技有限公司 | Pinyin identification method and device, storage medium and electronic equipment |
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